Overview

Dataset statistics

Number of variables9
Number of observations2247
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.1 KiB
Average record size in memory72.1 B

Variable types

DateTime1
Numeric8

Alerts

relative_humidity is highly correlated with absolute_humidityHigh correlation
absolute_humidity is highly correlated with relative_humidity and 2 other fieldsHigh correlation
sensor_1 is highly correlated with sensor_2 and 3 other fieldsHigh correlation
sensor_2 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
sensor_3 is highly correlated with absolute_humidity and 4 other fieldsHigh correlation
sensor_4 is highly correlated with absolute_humidity and 4 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
relative_humidity is highly correlated with absolute_humidityHigh correlation
absolute_humidity is highly correlated with relative_humidity and 2 other fieldsHigh correlation
sensor_1 is highly correlated with sensor_2 and 3 other fieldsHigh correlation
sensor_2 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
sensor_3 is highly correlated with absolute_humidity and 4 other fieldsHigh correlation
sensor_4 is highly correlated with absolute_humidity and 4 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
absolute_humidity is highly correlated with sensor_4High correlation
sensor_1 is highly correlated with sensor_2 and 3 other fieldsHigh correlation
sensor_2 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
sensor_3 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
sensor_4 is highly correlated with absolute_humidity and 4 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
deg_C is highly correlated with relative_humidity and 5 other fieldsHigh correlation
relative_humidity is highly correlated with deg_C and 2 other fieldsHigh correlation
absolute_humidity is highly correlated with deg_C and 4 other fieldsHigh correlation
sensor_1 is highly correlated with deg_C and 4 other fieldsHigh correlation
sensor_2 is highly correlated with deg_C and 5 other fieldsHigh correlation
sensor_3 is highly correlated with deg_C and 5 other fieldsHigh correlation
sensor_4 is highly correlated with deg_C and 6 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 3 other fieldsHigh correlation
date_time has unique values Unique

Reproduction

Analysis started2021-10-05 10:16:00.992789
Analysis finished2021-10-05 10:16:10.994515
Duration10 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date_time
Date

UNIQUE

Distinct2247
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
Minimum2011-01-01 00:00:00
Maximum2011-04-04 14:00:00
2021-10-05T12:16:11.076005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:11.221702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

deg_C
Real number (ℝ)

HIGH CORRELATION

Distinct280
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.80814419
Minimum-1.8
Maximum30.9
Zeros1
Zeros (%)< 0.1%
Negative13
Negative (%)0.6%
Memory size17.7 KiB
2021-10-05T12:16:11.380084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.8
5-th percentile2.9
Q15.6
median9.8
Q314.2
95-th percentile23.7
Maximum30.9
Range32.7
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation6.44449695
Coefficient of variation (CV)0.5962630435
Kurtosis-0.2825219874
Mean10.80814419
Median Absolute Deviation (MAD)4.3
Skewness0.6772607799
Sum24285.9
Variance41.53154094
MonotonicityNot monotonic
2021-10-05T12:16:11.504083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.230
 
1.3%
4.127
 
1.2%
3.925
 
1.1%
4.625
 
1.1%
3.423
 
1.0%
622
 
1.0%
6.722
 
1.0%
4.721
 
0.9%
6.521
 
0.9%
5.720
 
0.9%
Other values (270)2011
89.5%
ValueCountFrequency (%)
-1.81
< 0.1%
-1.32
0.1%
-1.22
0.1%
-1.11
< 0.1%
-0.62
0.1%
-0.51
< 0.1%
-0.31
< 0.1%
-0.21
< 0.1%
-0.12
0.1%
01
< 0.1%
ValueCountFrequency (%)
30.91
< 0.1%
30.31
< 0.1%
29.61
< 0.1%
29.41
< 0.1%
29.11
< 0.1%
28.22
0.1%
27.92
0.1%
27.51
< 0.1%
27.31
< 0.1%
27.22
0.1%

relative_humidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct653
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.03124166
Minimum9.8
Maximum88.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:11.648826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.8
5-th percentile24.93
Q136.9
median50.6
Q363.55
95-th percentile78.6
Maximum88.8
Range79
Interquartile range (IQR)26.65

Descriptive statistics

Standard deviation16.66504715
Coefficient of variation (CV)0.3265655823
Kurtosis-0.8112609995
Mean51.03124166
Median Absolute Deviation (MAD)13.4
Skewness0.05956760129
Sum114667.2
Variance277.7237965
MonotonicityNot monotonic
2021-10-05T12:16:11.781779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.613
 
0.6%
53.411
 
0.5%
70.210
 
0.4%
5610
 
0.4%
5110
 
0.4%
61.710
 
0.4%
34.110
 
0.4%
31.710
 
0.4%
42.79
 
0.4%
76.69
 
0.4%
Other values (643)2145
95.5%
ValueCountFrequency (%)
9.81
< 0.1%
10.41
< 0.1%
10.71
< 0.1%
12.71
< 0.1%
12.81
< 0.1%
13.21
< 0.1%
13.31
< 0.1%
13.51
< 0.1%
13.91
< 0.1%
14.21
< 0.1%
ValueCountFrequency (%)
88.81
< 0.1%
88.32
0.1%
88.11
< 0.1%
881
< 0.1%
872
0.1%
86.91
< 0.1%
86.41
< 0.1%
86.21
< 0.1%
86.11
< 0.1%
85.82
0.1%

absolute_humidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1915
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6270527815
Minimum0.1847
Maximum1.393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:11.918457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1847
5-th percentile0.23283
Q10.41335
median0.5964
Q30.80495
95-th percentile1.10381
Maximum1.393
Range1.2083
Interquartile range (IQR)0.3916

Descriptive statistics

Standard deviation0.266588167
Coefficient of variation (CV)0.4251447006
Kurtosis-0.5243672511
Mean0.6270527815
Median Absolute Deviation (MAD)0.1908
Skewness0.4674074027
Sum1408.9876
Variance0.07106925079
MonotonicityNot monotonic
2021-10-05T12:16:12.055457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23454
 
0.2%
0.23244
 
0.2%
0.72384
 
0.2%
0.23534
 
0.2%
0.23024
 
0.2%
0.23034
 
0.2%
0.68264
 
0.2%
0.23224
 
0.2%
0.22593
 
0.1%
0.54583
 
0.1%
Other values (1905)2209
98.3%
ValueCountFrequency (%)
0.18471
< 0.1%
0.18621
< 0.1%
0.1911
< 0.1%
0.19751
< 0.1%
0.20311
< 0.1%
0.20621
< 0.1%
0.20861
< 0.1%
0.21571
< 0.1%
0.22021
< 0.1%
0.2211
< 0.1%
ValueCountFrequency (%)
1.3931
< 0.1%
1.38381
< 0.1%
1.33421
< 0.1%
1.32242
0.1%
1.32191
< 0.1%
1.32081
< 0.1%
1.31821
< 0.1%
1.31711
< 0.1%
1.31541
< 0.1%
1.31351
< 0.1%

sensor_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1758
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106.53449
Minimum665.9
Maximum1882.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:12.181499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum665.9
5-th percentile829.08
Q1951.5
median1080.4
Q31222.1
95-th percentile1500.84
Maximum1882.9
Range1217
Interquartile range (IQR)270.6

Descriptive statistics

Standard deviation205.341455
Coefficient of variation (CV)0.1855716716
Kurtosis0.3067191872
Mean1106.53449
Median Absolute Deviation (MAD)133.4
Skewness0.7197817369
Sum2486383
Variance42165.11315
MonotonicityNot monotonic
2021-10-05T12:16:12.304457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9365
 
0.2%
1117.24
 
0.2%
1000.64
 
0.2%
952.84
 
0.2%
9094
 
0.2%
1068.94
 
0.2%
899.64
 
0.2%
1025.94
 
0.2%
1134.64
 
0.2%
1295.34
 
0.2%
Other values (1748)2206
98.2%
ValueCountFrequency (%)
665.91
< 0.1%
709.11
< 0.1%
713.51
< 0.1%
719.21
< 0.1%
722.21
< 0.1%
727.62
0.1%
727.72
0.1%
732.81
< 0.1%
736.61
< 0.1%
739.81
< 0.1%
ValueCountFrequency (%)
1882.91
< 0.1%
1842.81
< 0.1%
1838.61
< 0.1%
18221
< 0.1%
1815.81
< 0.1%
1797.61
< 0.1%
17951
< 0.1%
1783.71
< 0.1%
1780.81
< 0.1%
1762.81
< 0.1%

sensor_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1816
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836.4597686
Minimum356.2
Maximum1776.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:12.428499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum356.2
5-th percentile401.89
Q1640.7
median800.8
Q31016.1
95-th percentile1327.16
Maximum1776.1
Range1419.9
Interquartile range (IQR)375.4

Descriptive statistics

Standard deviation272.8165854
Coefficient of variation (CV)0.3261562548
Kurtosis-0.2489792653
Mean836.4597686
Median Absolute Deviation (MAD)185.4
Skewness0.448121094
Sum1879525.1
Variance74428.88926
MonotonicityNot monotonic
2021-10-05T12:16:12.541458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
377.510
 
0.4%
401.18
 
0.4%
373.57
 
0.3%
408.96
 
0.3%
416.85
 
0.2%
774.25
 
0.2%
6884
 
0.2%
691.84
 
0.2%
913.94
 
0.2%
412.94
 
0.2%
Other values (1806)2190
97.5%
ValueCountFrequency (%)
356.21
 
< 0.1%
364.31
 
< 0.1%
364.51
 
< 0.1%
364.71
 
< 0.1%
365.22
0.1%
365.53
0.1%
365.72
0.1%
365.81
 
< 0.1%
368.31
 
< 0.1%
368.41
 
< 0.1%
ValueCountFrequency (%)
1776.11
< 0.1%
1746.21
< 0.1%
17311
< 0.1%
1677.81
< 0.1%
1664.21
< 0.1%
1636.61
< 0.1%
1624.81
< 0.1%
16211
< 0.1%
1618.61
< 0.1%
15791
< 0.1%

sensor_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1833
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean828.3214953
Minimum320.1
Maximum1975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:12.670457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum320.1
5-th percentile447.43
Q1597.05
median757.1
Q3944.95
95-th percentile1737.6
Maximum1975
Range1654.9
Interquartile range (IQR)347.9

Descriptive statistics

Standard deviation339.5117785
Coefficient of variation (CV)0.4098792322
Kurtosis2.219109259
Mean828.3214953
Median Absolute Deviation (MAD)169.3
Skewness1.512811922
Sum1861238.4
Variance115268.2478
MonotonicityNot monotonic
2021-10-05T12:16:12.791490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666.46
 
0.3%
750.55
 
0.2%
824.75
 
0.2%
842.54
 
0.2%
5874
 
0.2%
7024
 
0.2%
538.64
 
0.2%
4524
 
0.2%
776.24
 
0.2%
719.14
 
0.2%
Other values (1823)2203
98.0%
ValueCountFrequency (%)
320.11
< 0.1%
325.21
< 0.1%
344.61
< 0.1%
351.91
< 0.1%
354.31
< 0.1%
3561
< 0.1%
3591
< 0.1%
3601
< 0.1%
3661
< 0.1%
366.32
0.1%
ValueCountFrequency (%)
19751
< 0.1%
1953.11
< 0.1%
1952.41
< 0.1%
1940.11
< 0.1%
19401
< 0.1%
1938.41
< 0.1%
1937.51
< 0.1%
1923.21
< 0.1%
1921.71
< 0.1%
19212
0.1%

sensor_4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1877
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1104.850601
Minimum523.4
Maximum2211.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:12.925467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum523.4
5-th percentile623.93
Q1899.45
median1076.2
Q31288.35
95-th percentile1620.22
Maximum2211.4
Range1688
Interquartile range (IQR)388.9

Descriptive statistics

Standard deviation293.1122248
Coefficient of variation (CV)0.2652958007
Kurtosis-0.01266502103
Mean1104.850601
Median Absolute Deviation (MAD)193.6
Skewness0.4236120482
Sum2482599.3
Variance85914.77634
MonotonicityNot monotonic
2021-10-05T12:16:13.060048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
989.84
 
0.2%
8934
 
0.2%
993.24
 
0.2%
956.54
 
0.2%
1226.14
 
0.2%
1030.24
 
0.2%
15193
 
0.1%
896.83
 
0.1%
635.63
 
0.1%
1018.43
 
0.1%
Other values (1867)2211
98.4%
ValueCountFrequency (%)
523.41
< 0.1%
559.71
< 0.1%
560.81
< 0.1%
561.61
< 0.1%
563.21
< 0.1%
563.31
< 0.1%
563.61
< 0.1%
5641
< 0.1%
564.51
< 0.1%
564.62
0.1%
ValueCountFrequency (%)
2211.41
< 0.1%
2185.91
< 0.1%
2073.81
< 0.1%
2068.61
< 0.1%
2065.21
< 0.1%
20251
< 0.1%
2016.11
< 0.1%
2004.51
< 0.1%
2003.41
< 0.1%
1989.81
< 0.1%

sensor_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2017
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1029.851535
Minimum218.8
Maximum2593.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2021-10-05T12:16:13.199289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum218.8
5-th percentile402.85
Q1688.55
median973.1
Q31324
95-th percentile1812.11
Maximum2593.8
Range2375
Interquartile range (IQR)635.45

Descriptive statistics

Standard deviation434.863287
Coefficient of variation (CV)0.4222582305
Kurtosis-0.2189117863
Mean1029.851535
Median Absolute Deviation (MAD)311.6
Skewness0.5342250443
Sum2314076.4
Variance189106.0784
MonotonicityNot monotonic
2021-10-05T12:16:13.326335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153.63
 
0.1%
585.93
 
0.1%
1043.53
 
0.1%
557.83
 
0.1%
796.13
 
0.1%
549.13
 
0.1%
15213
 
0.1%
910.43
 
0.1%
661.53
 
0.1%
1646.43
 
0.1%
Other values (2007)2217
98.7%
ValueCountFrequency (%)
218.81
< 0.1%
222.51
< 0.1%
2251
< 0.1%
229.71
< 0.1%
245.41
< 0.1%
251.91
< 0.1%
253.21
< 0.1%
2571
< 0.1%
2581
< 0.1%
259.41
< 0.1%
ValueCountFrequency (%)
2593.81
< 0.1%
2547.31
< 0.1%
2424.21
< 0.1%
2378.61
< 0.1%
2362.81
< 0.1%
2359.61
< 0.1%
2327.51
< 0.1%
2288.51
< 0.1%
2280.41
< 0.1%
2279.61
< 0.1%

Interactions

2021-10-05T12:16:09.631315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.228286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.210070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.279691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.315779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:06.517779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.586961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.613959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.746315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.349841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.338071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.406665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.433811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:06.653780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.714959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.738962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.877347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.481841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.483070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.547695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.560779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:06.798780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.853991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.875958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.990315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.603842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.619070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.678671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.681779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:06.934778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.982959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.004959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:10.104315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.719873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.748070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.802665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.797781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.063958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.104992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.124698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:10.230348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.842875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.882070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.934663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.928797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.200959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.232959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.255315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:10.351315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:02.971842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.018070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.067778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:06.061779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.336992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.363958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.384316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:10.477315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:03.099070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:04.154069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:05.201779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:06.209779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:07.466964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:08.493991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:16:09.515342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-05T12:16:13.654332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-05T12:16:13.823299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-05T12:16:13.989332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-05T12:16:14.157327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-05T12:16:10.666316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-05T12:16:10.902351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

date_timedeg_Crelative_humidityabsolute_humiditysensor_1sensor_2sensor_3sensor_4sensor_5
02011-01-01 00:00:008.041.30.43751108.8745.7797.1880.01273.1
12011-01-01 01:00:005.151.70.45641249.5864.9687.9972.81714.0
22011-01-01 02:00:005.851.50.46891102.6878.0693.7941.91300.8
32011-01-01 03:00:005.052.30.46931139.7916.2725.61011.01283.0
42011-01-01 04:00:004.557.50.46501022.4838.5871.5967.01142.3
52011-01-01 05:00:004.553.70.47591004.0745.5914.2989.1973.8
62011-01-01 06:00:003.354.80.4636940.9738.2816.0896.81049.4
72011-01-01 07:00:003.260.70.4667954.5713.9834.7935.6956.3
82011-01-01 08:00:002.565.70.4721969.9679.1943.8959.3892.0
92011-01-01 09:00:003.957.80.4807976.6655.5996.0906.0817.5

Last rows

date_timedeg_Crelative_humidityabsolute_humiditysensor_1sensor_2sensor_3sensor_4sensor_5
22372011-04-04 05:00:0010.761.70.7550941.3549.11098.5947.5549.1
22382011-04-04 06:00:009.566.90.7531989.8686.2805.61061.3841.6
22392011-04-04 07:00:009.160.70.74461411.71135.5474.71584.01515.3
22402011-04-04 08:00:0013.447.40.75531402.61389.2427.41652.61670.9
22412011-04-04 09:00:0018.937.80.74871284.01102.0501.91347.51567.2
22422011-04-04 10:00:0023.228.70.75681340.31023.9522.81374.01659.8
22432011-04-04 11:00:0024.522.50.71191232.8955.1616.11226.11269.0
22442011-04-04 12:00:0026.619.00.64061187.71052.4572.81253.41081.1
22452011-04-04 13:00:0029.112.70.51391053.21009.0702.01009.8808.5
22462011-04-04 14:00:0027.913.50.50281124.61078.4608.21061.3816.0